Update app.py

app.py

@@ -5,11 +5,16 @@ import cv2
 from keras.models import load_model
 from keras.models import Model
 from datasets import load_dataset
+from sklearn.cluster import KMeans
 
 autoencoder = load_model("autoencoder_model.keras")
 encoded_images = np.load("X_encoded_compressed.npy")
 dataset = load_dataset('eybro/images')
 
+num_clusters = 10  # Choose the number of clusters
+kmeans = KMeans(n_clusters=num_clusters, random_state=42)
+kmeans.fit(encoded_images)
+
 def find_nearest_neighbors(encoded_images, input_image, top_n=5):
     """
     Find the closest neighbors to the input image in the encoded image space.